Collaborative Research: Detection and Estimation of Multi-Scale Complex Spatiotemporal Processes in Tornadic Supercells from High Resolution Simulations and Multiparameter Radar

合作研究:通过高分辨率模拟和多参数雷达检测和估计龙卷超级单体中的多尺度复杂时空过程

基本信息

  • 批准号:
    2114817
  • 负责人:
  • 金额:
    $ 40.3万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-07-15 至 2025-06-30
  • 项目状态:
    未结题

项目摘要

The project is to understand thunderstorm conditions that trigger tornados. Each year across broad regions of the United States, atmospheric conditions become favorable for the formation of supercell thunderstorms, the most prolific source of violent tornadoes. Tornadoes ranked EF4 and EF5, the top strength categories of the Enhanced Fujita scale, are responsible for the bulk of fatalities, even though they are the least common, comprising less than 1% of observed tornadoes. The death and destruction wrought by supercell tornadoes has motivated much observational, theoretical, and numerical modeling research designed to understand and predict these powerful storms. However, despite the many advances that have resulted from these studies, there is currently poor understanding of what determines whether a supercell will produce a tornado or not, and whether that tornado, should it form at all, will be weak or strong, short-lived or long-lived. This complex question is not only one of the great mysteries of nature but is of critical importance to assuring public safety. The project will investigate these issues by combining observational, numerical, and analytical methods. The project will develop educational exhibits on tornadoes at the Fleet Science Center at Balboa Park, San Diego, CA and the National Weather Museum at Norman, OK. The project will also provide unique research and education opportunities for undergraduate and graduate students in understanding tornado evolution through high-resolution numerical simulations as well as data analysis and visualization. The central challenge for understanding the generation and maintenance of violent, long-track tornadoes in supercells is being able to quantify the storm-wide processes that determine whether strong, long-lived tornadoes form. This proposal will use a novel method called the Entropy Field Decomposition (EFD) as a unifying framework to integrate and quantify the complex dynamics of tornadic supercells produced in high resolution physics-based simulations, predicted radar signatures derived from these simulations, and actual observational data of supercells collected in the field. EFD is a data-agnostic approach to four-dimensional space-time entangled data mining that leverages techniques from Bayesian analysis and the physics theory of fields to identify statistically significant storm “modes" within huge volumes of complex, often noisy, data. In contrast with machine learning approaches, no training datasets are required. Rather, prior information within individual data derived from space-time correlations, codified in the theory of Entropy Spectrum Pathways (ESP), provides sufficient prior information to extract distinct space-time modes of complex systems. This method will be used to study a first-of-its-kind data set comprised of ensembles of high-resolution simulations that yield a rich variety of tornadic and non-tornadic storms to understand fundamental controls of tornadogenesis, tornadogenesis failure, and tornado maintenance. This ensemble will also enable some of the first detailed intercomparisons between mobile radar observations and tornado-resolving, idealized simulations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
该项目旨在了解引发龙卷风的雷暴条件。每年在美国的广大地区,大气条件变得有利于超级单体雷暴的形成,这是暴力龙卷风的最多产来源。EF4和EF5级龙卷风是增强型藤田等级的最高强度类别,它们是造成大部分死亡的原因,尽管它们是最不常见的,占观测到的龙卷风的不到1%。超级单体龙卷风造成的死亡和破坏激发了许多旨在理解和预测这些强大风暴的观测,理论和数值模拟研究。然而,尽管这些研究取得了许多进展,但目前对决定超级单体是否会产生龙卷风的因素以及龙卷风是否会形成,是弱还是强,短暂还是长久的了解甚少。这个复杂的问题不仅是大自然的最大奥秘之一,而且对确保公共安全至关重要。该项目将通过结合观测、数值和分析方法来调查这些问题。该项目将在加利福尼亚州圣地亚哥巴尔博亚公园的舰队科学中心和俄克拉荷马州诺曼的国家气象博物馆举办龙卷风教育展览。该项目还将为本科生和研究生提供独特的研究和教育机会,通过高分辨率数值模拟以及数据分析和可视化来了解龙卷风的演变。理解超级单体中暴力、长轨道龙卷风的产生和维持的核心挑战是能够量化风暴范围内的过程,这些过程决定了是否形成强大、长寿命的龙卷风。该提案将使用一种称为熵场分解(EFD)的新方法作为统一框架,以整合和量化高分辨率物理模拟中产生的龙卷风超级单体的复杂动力学,从这些模拟中预测的雷达特征,以及现场收集的超级单体的实际观测数据。EFD是一种数据不可知的四维时空纠缠数据挖掘方法,它利用贝叶斯分析和场的物理理论来识别大量复杂且经常有噪声的数据中统计上显著的风暴“模式”。与机器学习方法相比,不需要训练数据集。相反,在熵谱路径(ESP)理论中编码的来自时空相关性的个体数据内的先验信息提供了足够的先验信息来提取复杂系统的不同时空模式。这种方法将用于研究由高分辨率模拟组成的第一个数据集,这些模拟产生了丰富的龙卷风和非龙卷风风暴,以了解龙卷风发生,龙卷风发生失败和龙卷风维持的基本控制。这个集合也将使移动的雷达观测和龙卷风解析,理想化的模拟之间的一些第一次详细的相互比较。这个奖项反映了NSF的法定使命,并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Terrain effects on the 13 April 2018 Mountainburg, Arkansas EF2 tornado
地形对 2018 年 4 月 13 日阿肯色州芒廷堡 EF2 龙卷风的影响
Meteorological Research Enabled by Rapid-Scan Radar Technology
快速扫描雷达技术支持气象研究
  • DOI:
    10.1175/mwr-d-22-0324.1
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    3.2
  • 作者:
    Bodine, David J.;Griffin, Casey B.
  • 通讯作者:
    Griffin, Casey B.
Science Applications of Phased Array Radars
  • DOI:
    10.1175/bams-d-21-0173.1
  • 发表时间:
    2022-06
  • 期刊:
  • 影响因子:
    8
  • 作者:
    P. Kollias;R. Palmer;D. Bodine;T. Adachi;H. Bluestein;John Y. N. Cho;Casey B. Griffin;J. Houser;P. Kirstetter;M. Kumjian;J. Kurdzo;Wen-Chau Lee;E. Luke;S. Nesbitt;M. Oue;A. Shapiro;A. Rowe;J. Salazar;R. Tanamachi;Kristofer S. Tuftedal;Xuguang Wang;D. Zrnic;Bernat Puigdomènech Treserras
  • 通讯作者:
    P. Kollias;R. Palmer;D. Bodine;T. Adachi;H. Bluestein;John Y. N. Cho;Casey B. Griffin;J. Houser;P. Kirstetter;M. Kumjian;J. Kurdzo;Wen-Chau Lee;E. Luke;S. Nesbitt;M. Oue;A. Shapiro;A. Rowe;J. Salazar;R. Tanamachi;Kristofer S. Tuftedal;Xuguang Wang;D. Zrnic;Bernat Puigdomènech Treserras
A Primer on Phased Array Radar Technology for the Atmospheric Sciences
大气科学相控阵雷达技术入门
  • DOI:
    10.1175/bams-d-21-0172.1
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    8
  • 作者:
    Palmer, Robert;Bodine, David;Kollias, Pavlos;Schvartzman, David;Zrnić, Dusan;Kirstetter, Pierre;Zhang, Guifu;Yu, Tian-You;Kumjian, Matthew;Cheong, Boonleng
  • 通讯作者:
    Cheong, Boonleng
Exploring Tornadic Debris Signature Hypotheses Using Radar Simulations and Large-Eddy Simulations
使用雷达模拟和大涡模拟探索龙卷碎片特征假设
  • DOI:
    10.1175/jtech-d-22-0141.1
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    2.2
  • 作者:
    Cross, Rachael N.;Bodine, David J.;Palmer, Robert D.;Griffin, Casey;Cheong, Boonleng;Torres, Sebastian;Fulton, Caleb;Lujan, Javier;Maruyama, Takashi
  • 通讯作者:
    Maruyama, Takashi
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David Bodine其他文献

<strong>Novel lentivirus vectors for safe and efficient gene therapy of hemoglobin disorders</strong>
  • DOI:
    10.1016/j.bcmd.2007.10.020
  • 发表时间:
    2008-03-01
  • 期刊:
  • 影响因子:
  • 作者:
    David Bodine;Faith Harrow;Karina Laflame
  • 通讯作者:
    Karina Laflame

David Bodine的其他文献

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{{ truncateString('David Bodine', 18)}}的其他基金

Understanding the Relationship Between Tornadoes and Debris Through Observed and Simulated Radar Data
通过观测和模拟雷达数据了解龙卷风和碎片之间的关系
  • 批准号:
    1823478
  • 财政年份:
    2018
  • 资助金额:
    $ 40.3万
  • 项目类别:
    Continuing Grant
NSF East Asia and Pacific Summer Institute for FY 2012 in Japan
NSF 东亚及太平洋地区 2012 财年日本夏季学院
  • 批准号:
    1209444
  • 财政年份:
    2012
  • 资助金额:
    $ 40.3万
  • 项目类别:
    Fellowship Award

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Cell Research
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Cell Research (细胞研究)
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    30824808
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    2008
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    24.0 万元
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    专项基金项目
Research on the Rapid Growth Mechanism of KDP Crystal
  • 批准号:
    10774081
  • 批准年份:
    2007
  • 资助金额:
    45.0 万元
  • 项目类别:
    面上项目

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